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Embedded Systems & Edge AI Online Live Class by Experts

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Embedded Systems & Edge AI Online Live Classes in Saar | Free Demo Training

Embedded Systems & Edge AI - Syllabus, Fees & Duration

Introduction

The Embedded Systems & Edge AI course is a 6-month comprehensive program designed to provide practical knowledge in embedded programming, microcontrollers, IoT, Artificial Intelligence, Machine Learning, and Edge Computing. Students will learn to design and develop intelligent devices using hardware and AI technologies through real-world projects.

Course Syllabus

Month 1: Embedded Systems Fundamentals

Introduction to Embedded Systems

  • Introduction to Embedded Systems
  • Applications of Embedded Systems
  • Embedded Systems Architecture
  • Embedded Systems Design Process
  • Real-Time Embedded Systems Overview
  • Embedded Systems vs General Computing

Electronics Fundamentals

  • Basic Electronics Concepts
  • Analog and Digital Signals
  • Electronic Components
  • Resistors, Capacitors, and Diodes
  • Integrated Circuits
  • Circuit Design Basics

Microcontrollers

  • Introduction to Microcontrollers
  • Microcontroller Architecture
  • Arduino Platform
  • ESP32 Introduction
  • Raspberry Pi Overview
  • GPIO Programming

Month 2: Embedded Programming & Hardware Interfacing

Embedded C Programming

  • Introduction to Embedded C
  • Variables and Data Types
  • Operators and Expressions
  • Conditional Statements
  • Loops and Functions
  • Arrays and Pointers
  • Memory Management
  • Interrupt Programming

Hardware Interfacing

  • LED Interfacing
  • LCD Display Integration
  • Motor Control
  • Servo Motor Programming
  • Relay Modules
  • Sensor Integration
  • Camera and Display Modules

Communication Protocols

  • UART Communication
  • SPI Protocol
  • I2C Communication
  • CAN Protocol Basics
  • Bluetooth Communication

Month 3: IoT & Edge Computing

Internet of Things (IoT)

  • Introduction to IoT
  • IoT Architecture
  • IoT Devices and Applications
  • Sensor Networks
  • IoT Communication Models
  • IoT Security Basics

Edge Computing Fundamentals

  • Introduction to Edge Computing
  • Cloud Computing vs Edge Computing
  • Edge Device Architecture
  • Real-Time Data Processing
  • Edge AI Concepts

IoT Development

  • ESP32 IoT Applications
  • MQTT Protocol
  • IoT Cloud Platforms
  • Device Monitoring Systems
  • Remote Control Applications

Month 4: Artificial Intelligence & Machine Learning

AI and Machine Learning Basics

  • Introduction to Artificial Intelligence
  • Machine Learning Fundamentals
  • Supervised Learning
  • Unsupervised Learning
  • Data Collection and Processing
  • Model Training and Evaluation

Computer Vision

  • Introduction to Computer Vision
  • Image Processing Basics
  • OpenCV Fundamentals
  • Image Classification
  • Object Detection
  • Face Recognition

AI Model Deployment

  • Introduction to TensorFlow Lite
  • TinyML Basics
  • AI Model Optimization
  • Model Conversion for Edge Devices

Month 5: Advanced Edge AI & Robotics

TinyML Development

  • Introduction to Tiny Machine Learning
  • Machine Learning on Microcontrollers
  • Neural Networks for Edge Devices
  • Model Compression
  • Quantization Techniques

Edge AI Hardware Platforms

  • NVIDIA Jetson Platform Overview
  • Raspberry Pi AI Projects
  • Google Coral Edge TPU
  • AI Accelerator Concepts

Robotics Integration

  • Robot Control Systems
  • AI-Based Robotics
  • Sensor-Based Automation
  • Autonomous Systems

Month 6: Industry Projects & Applications

Real World Applications

  • Smart Home Automation
  • Industrial IoT Applications
  • Smart Agriculture Systems
  • Healthcare Monitoring Devices
  • AI Surveillance Systems

Final Projects

  • AI-Based Object Detection System
  • Face Recognition Attendance Device
  • Smart IoT Monitoring System
  • Voice Controlled AI Assistant
  • Autonomous Robot Using AI
  • Edge AI Smart Device Project

Tools and Technologies

  • Embedded C
  • Python
  • Arduino
  • ESP32
  • Raspberry Pi
  • TensorFlow Lite
  • TinyML
  • OpenCV
  • Machine Learning
  • IoT Platforms
  • MQTT
  • Linux
  • Git & GitHub

Download Syllabus - Embedded Systems & Edge AI
This syllabus is not final and can be customized as per needs/updates
 

    Course Highlights

  • Since 2001, Making IT Experts by Experts
  • Duration: 1000 Hours (Vary as per your skill)
  • Course Fees
  • Individual Live Classes (1:1)
  • Batch Classes @ Low Fees
  • Training on Your Time, Any Where
  • Access to Recorded Videos
  • Practical Internship on Projects
  • 100% Placement Support by our jobsNEAR.in
  • Training/Internship Certificate
  • Our Locations

10000+
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50+
25+

Embedded Systems & Edge AI Jobs in Saar

Enjoy the demand

Find jobs related to Embedded Systems & Edge AI in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Saar, chennai and europe countries. You can find many jobs for freshers related to the job positions in Saar.

  • Embedded Systems Engineer
  • Edge AI Engineer
  • IoT Engineer
  • Embedded Software Developer
  • Firmware Engineer
  • Robotics Engineer
  • AI Engineer
  • Computer Vision Engineer
  • TinyML Engineer
  • Hardware Design Engineer

Embedded Systems & Edge AI Internship/Course Details

Embedded Systems & Edge AI internship jobs in Saar
Embedded Systems & Edge AI Students will learn to develop intelligent devices using microcontrollers, sensors, AI models, and real-time processing technologies. The course covers embedded programming, hardware interfacing, computer vision, TinyML, robotics, and AI deployment on edge devices through hands-on projects. . Join our **Embedded Systems & Edge AI Training in Saar** to build industry-ready skills and prepare for careers in embedded development, IoT, robotics, and AI-based technology solutions. **Course Description (Use Saar)** Our **Embedded Systems & Edge AI Course in Saar** is a 6-month advanced training program designed to provide practical knowledge in embedded systems, IoT, Artificial Intelligence, Machine Learning, and Edge Computing.

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